Automated identification of sugar beet diseases using smartphones

Plant Pathology

Volumn 67, Issue 2, 2018, Pages 399-410

  Hallau, L ^a   Neumann, M ^a,b   Klatt, B ^b   Kleinhenz, B ^c   Klein, T ^c   Kuhn, C ^c   Rohrig M ^d   Bauckhage, C ^a   Kersting, K ^e   Mahlein, A K ^a   Steiner, U ^a   Oerke, E C ^a  

^a UNIVERSITY OF BONN   (Germany)

^b WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^c ZEPP   (Germany)

^d ISIP EV   (Germany)

^e TU DORTMUND UNIVERSITY   (Germany)

Author keywords

classification algorithm; disease identification; erosion band signature; RGB images; sugar beet

Indexed keywords

ABIOTIC FACTOR; ACCURACY ASSESSMENT; ALGORITHM; CROP YIELD; DETECTION METHOD; DISEASE CONTROL; FUNGAL DISEASE; IDENTIFICATION METHOD; IMAGE ANALYSIS; INTEGRATED APPROACH; MOBILE PHONE; MOLECULAR ANALYSIS; SUGAR BEET; SYMPTOM;

BACTERIA (MICROORGANISMS); BETA VULGARIS SUBSP. VULGARIS; CERCOSPORA; PHOMA; RAMULARIA;

EID: 85040809810     PISSN: 00320862     EISSN: 13653059     Source Type: Journal    
DOI: 10.1111/ppa.12741     Document Type: Article

References (48)

1. Abdullah NE, Rahim AA, Hashim H et al., 2007. Classification of rubber tree leaf diseases using multilayer perceptron neural network. In: 5th Student Conference on Research and Development. Los Alamitos, CA, USA: IEEE, 1–6. https://doi.org/10.1109/scored.2007.4451369.
2. Afridi MJ, Liu X, McGrath JM, 2014. An automated system for plant-level disease rating in real fields. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR 2014). Los Alamitos, CA, USA: IEEE, 148–53. https://doi.org/10.1109/icpr.2014.35.
3. Al Bashish D, Braik M, Bani-Ahmad S, 2010. A framework for detection and classification of plant leaf and stem diseases. In: International Conference on Signal and Image Processing (ICSIP). Los Alamitos, CA, USA: IEEE, 113–8. https://doi.org/10.1109/icsip.2010.5697452.
4. Anthonys G, Wickramarachchi N, 2009. An image recognition system for crop disease identification of paddy fields in Sri Lanka. In: International Conference on Industrial and Information Systems (ICIIS). Los Alamitos, CA, USA: IEEE, 403–7. https://doi.org/10.1109/iciinfs.2009.5429828.
5. Atoum Y, Afridi MJ, Liu X et al., 2016. On developing and enhancing plant-level disease rating systems in real fields. Pattern Recognition 53, 287–99.
6. Barbedo JCA, 2014. An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Disease 98, 1709–16.
7. Barbedo JCA, 2016. A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering 144, 52–60.
8. Bauckhage C, 2006. Tree-based signatures for shape classification. In: Proceedings of the International Conference on Image Processing (ICIP-06). Los Alamitos, CA, USA: IEEE, 2105–8. https://doi.org/10.1109/icip.2006.312823.
9. Bauer SD, Korc F, Förstner W, 2011. The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precision Agriculture 12, 361–77.
10. Behmann J, Mahlein AK, Rumpf T et al., 2015. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture 16, 239–60.
11. Bock CH, Poole GH, Parker PE et al., 2010. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences 29, 59–107.
12. Camargo A, Smith JS, 2009. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering 102, 9–21.
13. Crous PW, Braun U, 2003. Mycosphaerella and its Anamorphs. 1. Names Published in Cercospora and Passalora. CBS Biodiversity Series 1. Utrecht, Netherlands: CBS.
14. Cui D, Zhang Q, Li M et al., 2010. Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosystems Engineering 107, 186–93.
15. De Coninck BMA, Amand O, Delauré SL et al., 2012. The use of digital image analysis and real-time PCR fine-tunes bioassays for quantification of cercospora leaf spot disease in sugar beet breeding. Plant Pathology 61, 76–84.
16. Dutta B, Ingram T, Gitaitis RD et al., 2014. First report of bacterial blight of sugar beet caused by Pseudomonas syringae pv. aptata in Georgia, USA. Plant Disease 98, 1423.
17. Ebrahim Y, Ahmed M, Chau S et al., 2007. An efficient shape representation and description technique. In: Proceedings of the International Conference on Image Processing (ICIP-07). Los Alamitos, CA, USA: IEEE, 441–4. https://doi.org/10.1109/icip.2007.4379616.
18. Gonzales R, Woods R, 2008. Digital Image Processing. 3rd edn. Upper Saddle River, NJ, USA: Pearson Prentice Hall.
19. Harveson RM, Hanson LE, Hein GL, 2009. Compendium of Beet Diseases and Pests. 2nd edn. St Paul, MN, USA: APS Press.
20. Huang KY, 2007. Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Computers and Electronics in Agriculture 57, 3–11.
21. Hughes DP, Salathé M, 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. [https://arxiv.org/ftp/arxiv/papers/1511/1511.08060.pdf]. Accessed 7 June 2017.
22. Jian Z, Wei Z, 2010. Support vector machine for recognition of cucumber leaf diseases. In: 2nd International Conference on Advanced Computer Control, Vol. 5. Piscataway, NJ, USA: IEEE Press, 264–6.
23. Kai S, Zhikun L, Hang S et al., 2011. A research of maize disease image recognition of corn based on BP networks. In: 3rd International Conference on Measuring Technology and Mechatronics Automation. Los Alamitos, CA, USA: IEEE, 246–9. https://doi.org/10.1109/icmtma.2011.66.
24. Kohavi R, Provost F, 1998. Glossary of terms. Machine Learning 30, 271–4.
25. Maerlaender B, Hoffmann C, Koch HJ et al., 2003. Environmental situation and yield performance of the sugar beet crop in Germany: heading for sustainable development. Journal of Agronomy & Crop Science 189, 201–26.
26. Mahlein AK, Oerke EC, Steiner U et al., 2012. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology 133, 197–209.
27. Maturana D, Mery D, Soto A, 2009. Face recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification. In: 2009 International Conference of the Chilean Computer Science Society (SCCC-09). Los Alamitos, CA, USA: IEEE, 125–32. https://doi.org/10.1109/sccc.2009.21.
28. Meunkaewjinda A, Kumsawat P, Attakitmongcol K et al., 2008. Grape leaf disease detection from color imagery using hybrid intelligent system. In: 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Los Alamitos, CA, USA: IEEE, 513–6. https://doi.org/10.1109/ecticon.2008.4600483.
29. Neumann M, Hallau L, Klatt B, Kersting K, Bauckhage C, 2014. Erosion band features for cell phone image based plant disease classification. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR 2014). Los Alamitos, CA, USA: IEEE, 3315–20.
30. Nutter FW Jr, Gleason ML, Jenco JH, Christians NL, 1993. Accuracy, intrarater repeatability, and interrater reliability of disease assessment systems. Phytopathology 83, 806–12.
31. Nutter FW Jr, Esker PD, Coelho Netto RA, 2006. Disease assessment concepts in plant pathology. European Journal of Plant Pathology 115, 95–103.
32. Ojala T, Pietikainen M, Maenpaa T, 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–87.
33. Patil SB, Bodhe SK, 2011. Leaf disease severity measurement using image processing. International Journal of Engineering and Technology 3, 297–301.
34. Racca P, Joerg E, 2007. CERCBET3 a forecaster for epidemic development of Cercospora beticola. EPPO Bulletin 37, 344–9.
35. Racca P, Zeuner T, Jung J et al., 2010. Model validation and use of Geographic Information Systems in crop protection warning service. In: Oerke EC, Gerhards R, Menz G, Sikora RA, eds. Precision Crop Protection – The Challenge and Use of Heterogeneity. Dordrecht, Netherlands: Springer, 259–76.
36. Riekmann W, Steck U, 1995. Krankheiten und Schaedlinge der Zuckerruebe. Gelsenkirchen, Germany: Verlag Th. Mann.
37. Ruppel EG, Gaskill J, 1971. Techniques for evaluating sugarbeet for resistance to Cercospora beticola in the field. Journal of Sugar Beet Research 16, 384–9.
38. Sena DG, Pinto FAC, Queiroz DM et al., 2003. Fall armyworm damaged maize plant identification using digital images. Biosystems Engineering 85, 449–54.
39. Skaloudova B, Krvan V, Zemek R, 2006. Computer-assisted estimation of leaf damage caused by spider mites. Computers and Electronics in Agriculture 53, 81–91.
40. Skaracis GN, Pavli OI, Biancardi E, 2010. Cercospora leaf spot disease of sugar beet. Sugar Technology 12, 220–8.
41. Thach T, Munk L, Hansen AL et al., 2013. Disease variation and chemical control of Ramularia leaf spot in sugar beet. Crop Protection 51, 68–76.
42. Vazquez E, Yang X, Slabaugh GG, 2013. Erosion band signatures for spatial extraction of features. Machine Vision and Applications 24, 695–705.
43. Vereijssen J, Schneider JHM, Termorshuizen AJ, Jeger MJ, 2003. Comparison of two disease assessment methods for assessing cercospora leaf spot in sugar beet. Crop Protection 22, 201–9.
44. Weiland J, Koch G, 2004. Pathogen profile: sugarbeet leaf spot disease (Cercospora beticola Sacc.). Molecular Plant Pathology 5, 157–66.
45. Wolf P, Verreet J, 2002. The IPM sugar beet model. Plant Disease 86, 336–44.
46. Youwen T, Tianlai L, Yan N, 2008. The recognition of cucumber disease based on image processing and support vector machine. In: Congress on Image and Signal Processing (CISP ‘08). Los Alamitos, CA, USA: IEEE, 262–7. https://doi.org/10.1109/cisp.2008.29.
47. Zhou R, Kaneko S, Tanaka F, Kayamori M, Shimizu M, 2014. Disease detection of Cercospora leaf spot by robust template matching. Computers and Electronics in Agriculture 108, 58–70.
48. Zhou R, Kaneko S, Tanaka F, Kayamori M, Shimizu M, 2015. Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition. Computers and Electronics in Agriculture 116, 65–79.